The environment of a remote sensing system for hyperspectral imagery (HSI) is well described in “Hyperspectral Image Processing for Automatic Target Detection Applications” by Manolakis, D., Marden, D., and Shaw G. (Lincoln Laboratory Journal; Volume 14; 2003 pp. 79-82). An imaging sensor has pixels that record a measurement of hyperspectral energy. An HSI device will record the energy in an array of pixels that captures spatial information by the geometry of the array and captures spectral information by making measurements in each pixel of a number of contiguous hyperspectral bands. Further processing of the spatial and spectral information depends upon a specific application of the remote sensing system.
Remotely sensed HSI has proven to be valuable for wide ranging applications including environmental and land use monitoring, military surveillance and reconnaissance. HSI provides image data that contains both spatial and spectral information. These types of information can be used for remote detection and tracking tasks. Specifically, given a set of visual sensors mounted on a platform such as an unmanned aerial vehicle (UAV) or ground station, a video of HSI may be acquired and a set of algorithms may be applied to the spectral video to detect and track objects from frame to frame.
Spectral-based processing algorithms have been developed to classify or group similar pixels; that is, pixels with similar spectral characteristics or signatures. Processing in this manner alone is not amenable to target tracking and detection applications where the number and size of targets in a scene is typically too small to support the estimation of statistical properties necessary to classify the type of target. However, spatial processing of typical HSI is compromised by the low spatial resolution of typical systems that collect HSI. As a result, remote sensing systems that collect and process HSI are typically developed as a trade-off between spectral and spatial resolution to maximize detection of both resolved and unresolved targets where a resolved target is an object imaged by more than one pixel. In this way, spectral techniques can detect unresolved targets by their signature and spatial techniques can detect resolved targets by their shape.
A number of hyperspectral search algorithms have been developed and used in the processing of HSI for the purpose of target detection. These hyperspectral search algorithms are typically designed to exploit statistical characteristics of candidate targets in the imagery and are typically built upon well-known statistical concepts. For example, Mahalanobis distance is a statistical measure of similarity that has been applied to hyperspectral pixel signatures. Mahalanobis distance measures a signature's similarity by testing the signature against an average and standard deviation of a known class of signatures.
Other known techniques include Spectral Angle Mapping (SAM), Spectral Information Divergence (SID), Zero Mean Differential Area (ZMDA) and Bhattacharyya Distance. SAM is a method for comparing a candidate target's signature to a known signature by treating each spectra as vectors and calculating the angle between the vectors. Because SAM uses only the vector direction and not the vector length, the method is insensitive to variation in illumination. SID is a method for comparing a candidate target's signature to a known signature by measuring the probabilistic discrepancy or divergence between the spectra. ZMDA normalizes the candidate target's and known signatures by their variance and computes their difference, which corresponds to the area between the two vectors. Bhattacharyya Distance is similar to Mahalanobois Distance but is used to measure the distance between a set of candidate target signatures against a known class of signatures.